Abstract Cardiovascular disease (CVD) is the leading global cause of death, highlighting the urgent need for early, accurate, and interpretable diagnostic tools. However, many AI-based heart disease prediction models lack transparency, hindering their acceptance in clinical settings. This study proposes XAI-HD, a hybrid framework integrating machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques for heart disease detection. The framework systematically addresses key challenges, including class imbalance, missing data, and feature inconsistency, through advanced preprocessing and class-balancing methods such as OSS, NCR, SMOTEN, ADASYN, SMOTETomek, and SMOTEENN. Comparative performance evaluations across multiple datasets (CHD, FHD, SHD) demonstrate that XAI-HD reduces classification error rates by 20–25% compared to traditional ML-based models, achieving superior accuracy, precision, recall, and F1-score. Additionally, SHAP and LIME-based feature importance analysis enhances model interpretability, fostering trust among medical professionals. The proposed framework holds significant real-world applicability, including seamless integration i
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